
"""simple docstring"""






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase        =				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase        =				[]
for i in range(6):
					# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
					)
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
					)
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
					# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
					)
					rename_keys.append(
					    (
					        f"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
					        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
					    )
					)
					rename_keys.append(
					    (
					        f"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
					        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
					    )
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

					# q, k, v projections in self/cross-attention in decoder for conditional DETR
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
					)

					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
					)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		List[str]			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  state_dict.pop(_lowerCamelCase			)
	lowerCAmelCase												=  val




def __magic_name__	(				_lowerCamelCase:		Optional[int]			)					->						Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			lowerCAmelCase												=  key.replace('''backbone.0.body''',					'''backbone.conv_encoder.model'''			)
			lowerCAmelCase												=  value
		else:
			lowerCAmelCase												=  value

	return new_state_dict




def __magic_name__	(				_lowerCamelCase:		str,					_lowerCamelCase:		str=False			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  ''''''
	if is_panoptic:
		lowerCAmelCase												=  '''conditional_detr.'''

	# first: transformer encoder
	for i in range(6			):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		lowerCAmelCase												=  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
		lowerCAmelCase												=  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""			)
		# next, add query, keys and values (in that order) to the state dict
		lowerCAmelCase												=  in_proj_weight[:256, :]
		lowerCAmelCase												=  in_proj_bias[:256]
		lowerCAmelCase												=  in_proj_weight[256:512, :]
		lowerCAmelCase												=  in_proj_bias[256:512]
		lowerCAmelCase												=  in_proj_weight[-256:, :]
		lowerCAmelCase												=  in_proj_bias[-256:]




def __magic_name__	(				)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase												=  Image.open(requests.get(_lowerCamelCase,					stream=_lowerCamelCase			).raw			)

	return im




@torch.no_grad()
def __magic_name__	(				_lowerCamelCase:		Any,					_lowerCamelCase:		Dict			)					->						Optional[int]:

	'''simple docstring'''




	lowerCAmelCase												=  ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		lowerCAmelCase												=  '''resnet101'''
	if "dc5" in model_name:
		lowerCAmelCase												=  True
	lowerCAmelCase												=  '''panoptic''' in model_name
	if is_panoptic:
		lowerCAmelCase												=  250
	else:
		lowerCAmelCase												=  91
		lowerCAmelCase												=  '''huggingface/label-files'''
		lowerCAmelCase												=  '''coco-detection-id2label.json'''
		lowerCAmelCase												=  json.load(open(hf_hub_download(_lowerCamelCase,					_lowerCamelCase,					repo_type='''dataset'''			),					'''r'''			)			)
		lowerCAmelCase												=  {int(_lowerCamelCase			): v for k, v in idalabel.items()}
		lowerCAmelCase												=  idalabel
		lowerCAmelCase												=  {v: k for k, v in idalabel.items()}

	# load image processor
	lowerCAmelCase												=  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
	lowerCAmelCase												=  ConditionalDetrImageProcessor(format=_lowerCamelCase			)

	# prepare image
	lowerCAmelCase												=  prepare_img()
	lowerCAmelCase												=  image_processor(images=_lowerCamelCase,					return_tensors='''pt'''			)
	lowerCAmelCase												=  encoding['''pixel_values''']

	logger.info(F"""Converting model {model_name}..."""			)

	# load original model from torch hub
	lowerCAmelCase												=  torch.hub.load('''DeppMeng/ConditionalDETR''',					_lowerCamelCase,					pretrained=_lowerCamelCase			).eval()
	lowerCAmelCase												=  conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			lowerCAmelCase												=  '''conditional_detr.''' + src
		rename_key(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
	lowerCAmelCase												=  rename_backbone_keys(_lowerCamelCase			)
	# query, key and value matrices need special treatment
	read_in_q_k_v(_lowerCamelCase,					is_panoptic=_lowerCamelCase			)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	lowerCAmelCase												=  '''conditional_detr.model.''' if is_panoptic else '''model.'''
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('''conditional_detr'''			)
			    and not key.startswith('''class_labels_classifier'''			)
			    and not key.startswith('''bbox_predictor'''			)
			):
				lowerCAmelCase												=  state_dict.pop(_lowerCamelCase			)
				lowerCAmelCase												=  val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				lowerCAmelCase												=  state_dict.pop(_lowerCamelCase			)
				lowerCAmelCase												=  val
			elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
				continue
			else:
				lowerCAmelCase												=  state_dict.pop(_lowerCamelCase			)
				lowerCAmelCase												=  val
		else:
			if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
				lowerCAmelCase												=  state_dict.pop(_lowerCamelCase			)
				lowerCAmelCase												=  val
    # finally, create HuggingFace model and load state dict
	lowerCAmelCase												=  ConditionalDetrForSegmentation(_lowerCamelCase			) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase			)
	model.load_state_dict(_lowerCamelCase			)
	model.eval()
	model.push_to_hub(repo_id=_lowerCamelCase,					organization='''DepuMeng''',					commit_message='''Add model'''			)
	# verify our conversion
	lowerCAmelCase												=  conditional_detr(_lowerCamelCase			)
	lowerCAmelCase												=  model(_lowerCamelCase			)
	assert torch.allclose(outputs.logits,					original_outputs['''pred_logits'''],					atol=1E-4			)
	assert torch.allclose(outputs.pred_boxes,					original_outputs['''pred_boxes'''],					atol=1E-4			)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks,					original_outputs['''pred_masks'''],					atol=1E-4			)

	# Save model and image processor
	logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""			)
	Path(_lowerCamelCase			).mkdir(exist_ok=_lowerCamelCase			)
	model.save_pretrained(_lowerCamelCase			)
	image_processor.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()

					parser.add_argument(
					    """--model_name""",
					    default="""conditional_detr_resnet50""",
					    type=str,
					    help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
					)
					UpperCAmelCase        =				parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	str:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[int]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	int      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	int      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	Dict:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Optional[int]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Dict      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	List[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Tuple      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Optional[Any]      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)


